Domain-Invariant Region Proposal Network For Cross-Domain Detection
2020 IEEE International Conference on Multimedia and Expo (ICME)(2020)
摘要
The performances of object detectors are highly impacted by the discrepancy between existing data sets and application scenarios, leading to the so-called domain shift problem. Previous works, based on Faster R-CNN, focus on aligning the image-level features and the region-level features. However, the Region Proposal Network (RPN), as a key module between the image-level and the region-level modules, still has the problem of domain shift that leads to inaccurate or even false detected results. To tackle this issue, we propose a new design, Domain-Invariant RPN (DIR), which adopts adversarial learning to eliminate the domain shift in RPN, and thereby, significantly improving the accuracy and robustness of bounding box proposals. Furthermore, we propose a Double-Consistency Regularization (DCR) to improve the overall feature alignment. Extensive experiments show that our approach outperforms state-of-the-art methods.
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关键词
Domain adaptation,Object detection,Adversarial learning,Domain classifier.
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